Computer-Aided Conceptual Design Through TRIZ-based Manipulation of Topological Optimizations

Organised by: Cranfield UniversityIn a recent project the authors proposed the adoption of Optimization Systems [1] as a bridging element between Computer-Aided Innovation (CAI) and PLM to identify geometrical contradictions [2], a particular case of the TRIZ physical contradiction [3]. A further development of the research has revealed that the solutions obtained from several topological optimizations can be considered as elementary customized modeling features for a specific design task. The topology overcoming the arising geometrical contradiction can be obtained through a manipulation of the density distributions constituting the conflicting pair. Already two strategies of density combination have been identified as capable to solve geometrical contradictions.Mori Seiki – The Machine Tool Compan

Using numerical plant models and phenotypic correlation space to design achievable ideotypes

Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, i.e. ideal values of a set of plant traits resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of a performance criteria (e.g. yield, light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi-objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for correlations between traits based on field observations. We applied this approach to two contrasting models: a process-based crop model of sunflower and a functional-structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modeling approach, which identified paths for desirable trait modification, including direction and intensity.Comment: 25 pages, 5 figures, 2017, Plant, Cell and Environmen

Energy efficiency parametric design tool in the framework of holistic ship design optimization

Recent International Maritime Organization (IMO) decisions with respect to measures to reduce the emissions from maritime greenhouse gases (GHGs) suggest that the collaboration of all major stakeholders of shipbuilding and ship operations is required to address this complex techno-economical and highly political problem efficiently. This calls eventually for the development of proper design, operational knowledge, and assessment tools for the energy-efficient design and operation of ships, as suggested by the Second IMO GHG Study (2009). This type of coordination of the efforts of many maritime stakeholders, with often conflicting professional interests but ultimately commonly aiming at optimal ship design and operation solutions, has been addressed within a methodology developed in the EU-funded Logistics-Based (LOGBASED) Design Project (2004–2007). Based on the knowledge base developed within this project, a new parametric design software tool (PDT) has been developed by the National Technical University of Athens, Ship Design Laboratory (NTUA-SDL), for implementing an energy efficiency design and management procedure. The PDT is an integral part of an earlier developed holistic ship design optimization approach by NTUA-SDL that addresses the multi-objective ship design optimization problem. It provides Pareto-optimum solutions and a complete mapping of the design space in a comprehensive way for the final assessment and decision by all the involved stakeholders. The application of the tool to the design of a large oil tanker and alternatively to container ships is elaborated in the presented paper

Optimizacón muti-objetivo aplicada a problemas reales de ingeniería civil

En las dos últimas décadas se han producido muchos avances en el campo de la optimización multi-objetivo con metaheurísticas, pero han sido pocos los trabajos que han abordado problemas de ingeniería civil del mundo real, como el dimensionamiento integral de estructuras de barras espaciales que incluyen nodos rígidos, materiales distintos y efectos de segundo orden. El dimensionamiento integral de una estructura civil en una sola etapa, es decir, determinar los parámetros geométricos de las secciones transversales de todos los elementos que componen la estructura, se vuelve cada vez más complejo cuando el tamaño de la estructura crece. Éste es un problema de optimización multi-objetivo con restricciones porque si se quiere reducir costes económicos (en términos de la cantidad de material utilizado) no se puede hacer sin tener en cuenta las deformaciones que pueden dejar a la estructura fuera de servicio. En este punto entran en juego las restricciones que limitan las soluciones para que la estructura sea estable, garantizando la resistencia de los materiales y las proporciones geométricas (espesor - altura de las placas y ancho - alto de las barras). En este contexto, se ha realizado una revisión del estado del arte que ha dado lugar a la publicación: "A Survey of Multi-objective Metaheuristics. Applied to Structural Optimization. Structural and Multidisciplinary Optimization. Volumen 59, Número 4, páginas: 537-558. 2013", donde se han recopilado 58 artículos relevantes desde 1992 hasta 2012. También se ha propuesto una clasificación con la que se ha logrado agrupar y determinar la complejidad de los problemas y que técnicas metaheurísticas empleadas para el diseño estructural. Las conclusiones alcanzadas han sido que se ha investigado poco sobre el comportamiento de las técnicas metaheurísticas de optimización multi-objetivo para resolver problemas como los planteados en este trabajo de tesis, así como que tampoco se han utilizado técnicas recientes. Para poder investigar sobre metaheurísticas multi-objetivo y la resolución de problemas de estructuras civiles ha sido necesario de implementar nuevas herramientas software que no estaban disponibles. El enfoque seguido ha sido combinar un software de diseño de estructuras realizado por el doctorando llamado Ebes (Estructuras de Barras Espaciales) con el framework de optimización multi-objetivo jMetal. El resultado ha sido la herramienta jMetal+EBEs, que se ha publicado en "Integrating a Multi-Objective Optimization Framework Into a Structural Design Software. Advances in Engineering Software. Volumen 76, páginas: 161-170. Octubre 2014". Las líneas de investigaciones abiertas han propiciado investigar la factibilidad y eficiencia de los algoritmos para diseñar estructuras civiles de diferente complejidad. En este contexto, se han diseñado dos puentes atirantados de distinto tamaño, dando lugar a dos problemas de corte real, y se han abordado con un conjunto representativo de metaheurísticas multi-objetivo representativas del estado del arte. El estudio llevado a cabo se ha presentado en el artículo "Structural Design using Multiobjective Metaheuristics. Comparative Study and Application to a Real World Problem. Structural and Multidisciplinary Optimization. Aceptado el 21 de Junio de 2015." En el cuarto artículo que avala la tesis doctoral ("Distributed multiobjective metaheuristics for real-world structural optimization problems". Computer Journal. En prensa desde el 21 de Agosto de 2014) se ha realizado un estudio sobre una estructura civil de muy alta dimensionalidad, que consiste en un puente atirantado de más de 160 metros de largo. Abordar su resolución ha obligado a implementar metaheurísticas paralelas para poder usar un clúster de más de 400 núcleos, con el que se han obtenido resultados satisfactorios en unas horas que, de otra manera, usando un único ordenador, hubiera llevado más de medio año de cómputo

Design Space Exploration in Cyber-Physical Systems

Cyber physical systems (CPS) integrate a variety of engineering areas such as control, mechanical and computer engineering in a holistic design effort. While interdependencies between the different disciplines are key attributes of CPS design science, little is known about the impact of design decisions of the cyber part on the overall system qualities. To investigate these interdependencies, this paper proposes a simulation-based Design Space Exploration (DSE) framework that considers detailed cyber system parameters such as cache size, bus width, and voltage levels in addition to physical and control parameters of the CPS. We propose an exploration algorithm that surfs the parameter configurations in the cyber physical sub-systems, in order to approximate the Pareto-optimal design points with regards to the trade-os among the design objectives, such as energy consumption and control stability. We apply the proposed framework to a network control system for an inverted-pendulum application. The presented holistic evaluation of the identified Pareto-points reveals the presence of non-trivial trade-os, which are imposed by the control, physical, and detailed cyber parameters. For instance the identified energy and control optimal design points comprise configurations with a wide range of CPU speeds, sample times and cache configuration following non-trivial zig-zag patterns. The proposed framework could identify and manage those trade-os and, as a result, is an imperative rst step to automate the search for superior CSP configurations

Economic and environmental strategies for process design

This paper first addresses the definition of various objectives involved in eco-efficient processes, taking simultaneously into account ecological and economic considerations. The environmental aspect at the preliminary design phase of chemical processes is quantified by using a set of metrics or indicators following the guidelines of sustainability concepts proposed by . The resulting multiobjective problem is solved by a genetic algorithm following an improved variant of the so-called NSGA II algorithm. A key point for evaluating environmental burdens is the use of the package ARIANE™, a decision support tool dedicated to the management of plants utilities (steam, electricity, hot water, etc.) and pollutants (CO2, SO2, NO, etc.), implemented here both to compute the primary energy requirements of the process and to quantify its pollutant emissions. The well-known benchmark process for hydrodealkylation (HDA) of toluene to produce benzene, revisited here in a multiobjective optimization way, is used to illustrate the approach for finding eco-friendly and cost-effective designs. Preliminary biobjective studies are carried out for eliminating redundant environmental objectives. The trade-off between economic and environmental objectives is illustrated through Pareto curves. In order to aid decision making among the various alternatives that can be generated after this step, a synthetic evaluation method, based on the so-called Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) (), has been first used. Another simple procedure named FUCA has also been implemented and shown its efficiency vs. TOPSIS. Two scenarios are studied; in the former, the goal is to find the best trade-off between economic and ecological aspects while the latter case aims at defining the best compromise between economic and more strict environmental impact

Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria using Interactive Genetic Algorithms

This paper emphasizes the necessity of formally bringing qualitative and quantitative criteria of ergonomic design together, and provides a novel complementary design framework with this aim. Within this framework, different design criteria are viewed as optimization objectives; and design solutions are iteratively improved through the cooperative efforts of computer and user. The framework is rooted in multi-objective optimization, genetic algorithms and interactive user evaluation. Three different algorithms based on the framework are developed, and tested with an ergonomic chair design problem. The parallel and multi-objective approaches show promising results in fitness convergence, design diversity and user satisfaction metrics

Optimization as a design strategy. Considerations based on building simulation-assisted experiments about problem decomposition

In this article the most fundamental decomposition-based optimization method - block coordinate search, based on the sequential decomposition of problems in subproblems - and building performance simulation programs are used to reason about a building design process at micro-urban scale and strategies are defined to make the search more efficient. Cyclic overlapping block coordinate search is here considered in its double nature of optimization method and surrogate model (and metaphore) of a sequential design process. Heuristic indicators apt to support the design of search structures suited to that method are developed from building-simulation-assisted computational experiments, aimed to choose the form and position of a small building in a plot. Those indicators link the sharing of structure between subspaces ("commonality") to recursive recombination, measured as freshness of the search wake and novelty of the search moves. The aim of these indicators is to measure the relative effectiveness of decomposition-based design moves and create efficient block searches. Implications of a possible use of these indicators in genetic algorithms are also highlighted.Comment: 48 pages. 12 figures, 3 table

Cluster-Based Optimization of Cellular Materials and Structures for Crashworthiness

The objective of this work is to establish a cluster-based optimization method for the optimal design of cellular materials and structures for crashworthiness, which involves the use of nonlinear, dynamic finite element models. The proposed method uses a cluster-based structural optimization approach consisting of four steps: conceptual design generation, clustering, metamodel-based global optimization, and cellular material design. The conceptual design is generated using structural optimization methods. K-means clustering is applied to the conceptual design to reduce the dimensional of the design space as well as define the internal architectures of the multimaterial structure. With reduced dimension space, global optimization aims to improve the crashworthiness of the structure can be performed efficiently. The cellular material design incorporates two homogenization methods, namely, energy-based homogenization for linear and nonlinear elastic material models and mean-field homogenization for (fully) nonlinear material models. The proposed methodology is demonstrated using three designs for crashworthiness that include linear, geometrically nonlinear, and nonlinear models